Review Presentation for Will
A list of all the figures and tables to go into the biophysical connectivity review paper
Summary of all the data
#get a summary of all the data
summary(data.all)## Paper_ID DOI Title Lead_author
## Min. : 1.00 Length:344 Length:344 Length:344
## 1st Qu.:29.00 Class :character Class :character Class :character
## Median :57.00 Mode :character Mode :character Mode :character
## Mean :46.08
## 3rd Qu.:60.00
## Max. :78.00
##
## Institution Journal Published Motivation
## Length:344 Length:344 Min. :2010 Length:344
## Class :character Class :character 1st Qu.:2012 Class :character
## Mode :character Mode :character Median :2014 Mode :character
## Mean :2014
## 3rd Qu.:2015
## Max. :2016
##
## Oceanic_region Area Site
## Length:344 Length:344 Length:344
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## Size Years_total Date_start Date_end
## Length:344 Min. : 1.000 Min. :1960 Min. :1990
## Class :character 1st Qu.: 1.000 1st Qu.:1997 1st Qu.:2001
## Mode :character Median : 3.000 Median :1997 Median :2003
## Mean : 8.759 Mean :2001 Mean :2008
## 3rd Qu.: 5.000 3rd Qu.:2006 3rd Qu.:2009
## Max. :130.000 Max. :2070 Max. :2100
## NA's :4 NA's :26 NA's :26
## Run_mode Yearly_trends_compared Generic_species
## Length:344 Length:344 Mode :logical
## Class :character Class :character FALSE:154
## Mode :character Mode :character TRUE :190
## NA's :0
##
##
##
## Multiple_species Species_scientific_name Species_common_name
## Mode :logical Length:344 Length:344
## FALSE:180 Class :character Class :character
## TRUE :140 Mode :character Mode :character
## NA's :24
##
##
##
## Species_type Geographical_zone Model_reuse Model_name
## Length:344 Length:344 Mode :logical Length:344
## Class :character Class :character FALSE:50 Class :character
## Mode :character Mode :character TRUE :293 Mode :character
## NA's :1
##
##
##
## Model_type Model_movement Physical_model Nested_submodels
## Length:344 Length:344 Length:344 Mode :logical
## Class :character Class :character Class :character FALSE:307
## Mode :character Mode :character Mode :character TRUE :37
## NA's :0
##
##
##
## Model_resolution_min Model_spatial_shape Model_depth
## Min. : 0.005 Length:344 Min. : 6
## 1st Qu.: 1.850 Class :character 1st Qu.: 100
## Median : 4.000 Mode :character Median : 500
## Mean : 5.616 Mean :1319
## 3rd Qu.:10.000 3rd Qu.:4000
## Max. :33.000 Max. :6400
## NA's :2 NA's :199
## Tidal_model_used Bathymetry_model_used Model_integration
## Length:344 Mode :logical Length:344
## Class :character FALSE:269 Class :character
## Mode :character TRUE :75 Mode :character
## NA's :0
##
##
##
## Model_time_step Model_time_step_type Model_diffusion_scheme
## Min. : 50 Length:344 Length:344
## 1st Qu.: 300 Class :character Class :character
## Median : 3600 Mode :character Mode :character
## Mean : 9567
## 3rd Qu.: 4500
## Max. :86400
## NA's :181
## Model_diffusion_direction Model_diffusion_value PLD_type
## Length:344 Min. : 0.01 Length:344
## Class :character 1st Qu.: 50.00 Class :character
## Mode :character Median : 50.00 Mode :character
## Mean : 78.52
## 3rd Qu.: 60.00
## Max. :500.00
## NA's :160
## PLD_fixed PLD_variable PLD_stdev Spawning_period
## Min. : 2.00 Length:344 Min. :22 Length:344
## 1st Qu.: 20.00 Class :character 1st Qu.:22 Class :character
## Median : 30.00 Mode :character Median :22 Mode :character
## Mean : 38.19 Mean :22
## 3rd Qu.: 44.00 3rd Qu.:22
## Max. :420.00 Max. :22
## NA's :13 NA's :342
## Spawning_interval Spawning_release_sites Spawning_settlement_sites
## Length:344 Min. : 1.0 Min. : 1
## Class :character 1st Qu.: 8.0 1st Qu.: 19
## Mode :character Median : 40.0 Median : 302
## Mean : 209.6 Mean : 600
## 3rd Qu.: 61.0 3rd Qu.: 1002
## Max. :12397.0 Max. :12397
## NA's :8 NA's :38
## Spawning_depth_type Spawning_depth_value Spawning_depth_min
## Length:344 Min. : 0.00 Min. : 0.00
## Class :character 1st Qu.: 5.00 1st Qu.: 0.00
## Mode :character Median : 12.50 Median : 0.00
## Mean : 28.82 Mean : 17.24
## 3rd Qu.: 50.00 3rd Qu.: 10.00
## Max. :100.00 Max. :300.00
## NA's :304 NA's :270
## Spawning_depth_max Spawning_initiation Passive_movement
## Min. : 0.20 Length:344 Mode :logical
## 1st Qu.: 15.00 Class :character FALSE:91
## Median : 22.50 Mode :character TRUE :253
## Mean : 59.63 NA's :0
## 3rd Qu.: 60.00
## Max. :500.00
## NA's :270
## Diel_vertical_migration Circatidal_migration Pynocline_migration
## Mode :logical Mode :logical Mode :logical
## FALSE:300 FALSE:338 FALSE:338
## TRUE :44 TRUE :6 TRUE :6
## NA's :0 NA's :0 NA's :0
##
##
##
## Halocline_migration Ontogentic_vertical_migration
## Mode :logical Mode :logical
## FALSE:343 FALSE:316
## TRUE :1 TRUE :28
## NA's :0 NA's :0
##
##
##
## Vertical_swimming_ability Horizontal_swimming_ability Sinking_velocity
## Mode :logical Mode :logical Mode :logical
## FALSE:339 FALSE:331 FALSE:340
## TRUE :5 TRUE :13 TRUE :4
## NA's :0 NA's :0 NA's :0
##
##
##
## Egg_buoyancy Mortality Mortality_rate Mortality_function
## Mode :logical Mode :logical Length:344 Length:344
## FALSE:8 FALSE:204 Class :character Class :character
## TRUE :11 TRUE :140 Mode :character Mode :character
## NA's :325 NA's :0
##
##
##
## Orientation Orientation_value Growth Growth_func
## Mode :logical Length:344 Mode :logical Length:344
## FALSE:335 Class :character FALSE:321 Class :character
## TRUE :9 Mode :character TRUE :23 Mode :character
## NA's :0 NA's :0
##
##
##
## Sensory_ability Sensory_impl Sensory_extent
## Mode :logical Length:344 Min. : 1.000
## FALSE:143 Class :character 1st Qu.: 5.000
## TRUE :201 Mode :character Median :10.000
## NA's :0 Mean : 8.181
## 3rd Qu.:10.000
## Max. :50.000
## NA's :173
## Settlement_competency_window Settlement_competency_type
## Mode :logical Length:344
## FALSE:167 Class :character
## TRUE :168 Mode :character
## NA's :9
##
##
##
## Settlement_competency_factor Settlement_competency_type_start
## Length:344 Min. : 0.0
## Class :character 1st Qu.: 5.0
## Mode :character Median : 9.0
## Mean : 13.5
## 3rd Qu.: 20.0
## Max. :152.0
## NA's :177
## Settlement_site_type Settlement_site_size
## Length:344 Min. : 0.50
## Class :character 1st Qu.: 3.70
## Mode :character Median : 5.00
## Mean : 14.83
## 3rd Qu.: 11.00
## Max. :300.00
## NA's :233
## Particles_spawned_at_individual_type Particles_spawned_at_individual_site
## Length:344 Min. : 10
## Class :character 1st Qu.: 500
## Mode :character Median : 6800
## Mean : 10940204
## 3rd Qu.: 100000
## Max. :100000000
## NA's :39
## Particles_spawned_range_min Particles_spawned_range_max
## Min. : 1 Min. : 1000
## 1st Qu.: 1 1st Qu.: 1000
## Median : 100 Median : 1400
## Mean :1040 Mean : 2960
## 3rd Qu.: 100 3rd Qu.: 1400
## Max. :5000 Max. :10000
## NA's :339 NA's :339
## Particles_spawned_super_individual Particles_spawned_period
## Mode :logical Length:344
## FALSE:333 Class :character
## TRUE :11 Mode :character
## NA's :0
##
##
##
## Particles_spawned_total Replicated_run Sensitivity_analysis
## Min. :3.280e+02 Mode :logical Length:344
## 1st Qu.:6.100e+05 FALSE:337 Class :character
## Median :3.200e+06 TRUE :7 Mode :character
## Mean :6.815e+08 NA's :0
## 3rd Qu.:6.100e+07
## Max. :6.100e+09
## NA's :38
## Statistical_methods_used Empirically_validated Dispersal_kernel
## Length:344 Mode :logical Length:344
## Class :character FALSE:269 Class :character
## Mode :character TRUE :75 Mode :character
## NA's :0
##
##
##
## Temporal_kernel Accumulation_kernel Partial_summation
## Mode :logical Mode :logical Mode :logical
## FALSE:343 FALSE:343 FALSE:341
## TRUE :1 TRUE :1 TRUE :3
## NA's :0 NA's :0 NA's :0
##
##
##
## Minimum_arrival_time Mean_distance Median_distance
## Mode :logical Length:344 Mode :logical
## FALSE:337 Class :character FALSE:316
## TRUE :7 Mode :character TRUE :28
## NA's :0 NA's :0
##
##
##
## Distance_travelled_mean Trajectory_travelled_mean
## Min. : 9.1 Min. :171.1
## 1st Qu.: 34.0 1st Qu.:172.7
## Median : 78.2 Median :174.2
## Mean :161.2 Mean :174.2
## 3rd Qu.:230.0 3rd Qu.:175.7
## Max. :952.0 Max. :177.3
## NA's :291 NA's :342
## Distance_travelled_stdev Distance_travelled_median Direction_mean
## Length:344 Length:344 Mode :logical
## Class :character Class :character FALSE:339
## Mode :character Mode :character TRUE :5
## NA's :0
##
##
##
## Depth_mean Distance_travelled_upper_quantile Distance_travelled_max
## Mode :logical Mode :logical Length:344
## FALSE:339 FALSE:317 Class :character
## TRUE :5 TRUE :27 Mode :character
## NA's :0 NA's :0
##
##
##
## Distance_travelled_min Biophysical_matrix Travel_time_mean
## Length:344 Mode :logical Mode :logical
## Class :character FALSE:341 FALSE:340
## Mode :character TRUE :3 TRUE :4
## NA's :0 NA's :0
##
##
##
## Isotropy Positive_area Seeded_area Centre_of_mass
## Min. :0.0700 Min. : 50.0 Min. :0.7260 Mode :logical
## 1st Qu.:0.0950 1st Qu.: 92.5 1st Qu.:0.7290 FALSE:341
## Median :0.3000 Median :135.0 Median :0.7365 TRUE :3
## Mean :0.2386 Mean :126.3 Mean :0.7372 NA's :0
## 3rd Qu.:0.3650 3rd Qu.:164.5 3rd Qu.:0.7448
## Max. :0.3800 Max. :194.0 Max. :0.7500
## NA's :337 NA's :341 NA's :340
## Aggregation_index Mean_length Connectance
## Min. :0.3800 Min. :16.25 Length:344
## 1st Qu.:0.4025 1st Qu.:16.44 Class :character
## Median :0.4250 Median :16.62 Mode :character
## Mean :0.4250 Mean :16.62
## 3rd Qu.:0.4475 3rd Qu.:16.81
## Max. :0.4700 Max. :17.00
## NA's :342 NA's :342
## Proportion_sites_settled Connections_total Connected_clusters_total
## Mode :logical Mode :logical Mode :logical
## FALSE:335 FALSE:331 FALSE:337
## TRUE :9 TRUE :13 TRUE :7
## NA's :0 NA's :0 NA's :0
##
##
##
## Connected_clusters_largest_size Cross_shore_connectivity Dispersion_index
## Mode :logical Mode :logical Mode :logical
## FALSE:341 FALSE:343 FALSE:343
## TRUE :3 TRUE :1 TRUE :1
## NA's :0 NA's :0 NA's :0
##
##
##
## Connectivity_matrix_potential Connectivity_matrix_realised
## Length:344 Mode :logical
## Class :character FALSE:273
## Mode :character TRUE :71
## NA's :0
##
##
##
## Local_retention Local_retention_mean Local_retention_max Self_recruitment
## Mode :logical Min. :0.0000 Min. :0.130 Mode :logical
## FALSE:231 1st Qu.:0.0180 1st Qu.:0.385 FALSE:243
## TRUE :113 Median :0.0518 Median :0.695 TRUE :101
## NA's :0 Mean :0.0944 Mean :0.630 NA's :0
## 3rd Qu.:0.1070 3rd Qu.:0.940
## Max. :0.3510 Max. :1.000
## NA's :319 NA's :340
## Self_recruitment_mean Self_recruitment_max Self_recruitment_values
## Min. :0.0011 Min. :0.0400 Length:344
## 1st Qu.:0.0485 1st Qu.:0.1775 Class :character
## Median :0.1090 Median :0.4200 Mode :character
## Mean :0.2576 Mean :0.4713
## 3rd Qu.:0.4250 3rd Qu.:0.7662
## Max. :0.9800 Max. :1.0000
## NA's :293 NA's :312
## Settlement_success Settlement_success_mean Settlement_success_min
## Mode :logical Min. :0.00105 Min. :0.0010
## FALSE:295 1st Qu.:0.06200 1st Qu.:0.0069
## TRUE :49 Median :0.17000 Median :0.0275
## NA's :0 Mean :0.24761 Mean :0.1518
## 3rd Qu.:0.41000 3rd Qu.:0.1635
## Max. :0.80000 Max. :0.6700
## NA's :291 NA's :336
## Directional_exchange_rate Export_probability Source_sink_indicies
## Mode :logical Mode :logical Length:344
## FALSE:339 FALSE:341 Class :character
## TRUE :5 TRUE :3 Mode :character
## NA's :0 NA's :0
##
##
##
## Graph_theory Survived_% Comments X140
## Mode :logical Length:344 Length:344 Length:344
## FALSE:226 Class :character Class :character Class :character
## TRUE :118 Mode :character Mode :character Mode :character
## NA's :0
##
##
##
## X141 X142 X143
## Length:344 Length:344 Length:344
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
## General summary section
The years the studies were published
data.all <- data.all %>% mutate(movement =
Circatidal_migration |
Pynocline_migration |
Halocline_migration |
Ontogentic_vertical_migration |
Vertical_swimming_ability |
Horizontal_swimming_ability |
Sinking_velocity |
Diel_vertical_migration) %>% mutate(settlement = Sensory_extent > 0)
##data.all.comparisons <- data.all %>% gather(Behaviours, Implemented, Passive_movement,movement)
data.all.comparisons <- data.all %>% gather(Behaviours, Implemented,Passive_movement,movement,Orientation,settlement)
data.papers.published <- data.all.comparisons %>% select(Paper_ID,Published,Behaviours,Implemented) %>% distinct(Paper_ID,Published,Behaviours,Implemented)
data.papers.published <- filter(data.papers.published,Implemented == TRUE)
ggplot(data.papers.published, aes(Published)) + geom_bar(aes(fill = Behaviours)) +labs( x = "Publication year", y = "Number of papers")
###Published studies for each model
source("sort_factor.R")
ggplot(data.all,aes(SortFactorBySize(DOI))) + geom_bar() + theme(axis.text.x=element_blank(),
axis.ticks.x=element_blank()) + xlab("Individual model runs per study")
###Different taxa
data.taxa <- select(data.all,Species_type) %>% na.omit()
ggplot(data=data.taxa,aes(SortFactorBySize(Species_type))) + geom_bar() + coord_flip() + ylab("Number of models using the taxa") + xlab("Taxa")
Oceangraphic regions
data.regions <- data.all %>% select(Paper_ID,Oceanic_region) %>% distinct(Paper_ID,Oceanic_region)
data.regions %>% group_by(Oceanic_region) %>% summarise (n = n()) %>% mutate(freq = n / sum(n))## # A tibble: 21 × 3
## Oceanic_region n freq
## <chr> <int> <dbl>
## 1 Baltic Sea 1 0.01369863
## 2 Bering Sea 2 0.02739726
## 3 Carribean Sea 5 0.06849315
## 4 Global 2 0.02739726
## 5 Gulf of California 4 0.05479452
## 6 Gulf of Mexico 5 0.06849315
## 7 Indian Ocean 3 0.04109589
## 8 Indo-Pacific 4 0.05479452
## 9 Mediterranean Sea 11 0.15068493
## 10 North Pacific 1 0.01369863
## # ... with 11 more rowsggplot(data.regions,aes(SortFactorBySize(Oceanic_region)),fill=gray) + geom_bar() + coord_flip() + xlab("Oceanographic region") + ylab("Number of papers per region")
Biophysical models used
data.models <- data.all %>% select(Paper_ID,Model_name) %>% distinct(Paper_ID,Model_name)
data.models %>% group_by(Model_name) %>% summarise (n = n()) %>% mutate(freq = n / sum(n))## # A tibble: 24 × 3
## Model_name n freq
## <chr> <int> <dbl>
## 1 AFS 1 0.01369863
## 2 AP 1 0.01369863
## 3 ARIANE 2 0.02739726
## 4 Ayata et al 2010 1 0.01369863
## 5 Baptista/Dietrich 1 0.01369863
## 6 CMS 9 0.12328767
## 7 Connie 2 0.02739726
## 8 Connie2 1 0.01369863
## 9 Delft-PART 1 0.01369863
## 10 DROG3D 1 0.01369863
## # ... with 14 more rowsPhysical models
This section compares outputs of the physical models ###Physical models used
data.model.ocean <- data.all %>% select(Paper_ID,Physical_model) %>% distinct(Paper_ID,Physical_model)
data.model.ocean %>% group_by(Physical_model) %>% summarise (n = n()) %>% mutate(freq = n / sum(n))## # A tibble: 36 × 3
## Physical_model n freq
## <chr> <int> <dbl>
## 1 AFS 1 0.01369863
## 2 AP (ROMS) 1 0.01369863
## 3 ARDIRC 1 0.01369863
## 4 AVISO 1 0.01369863
## 5 BRAN 4 0.05479452
## 6 CANOPA 1 0.01369863
## 7 CORSE-400 (MARS-3D) 1 0.01369863
## 8 Delft3D-FLOW 1 0.01369863
## 9 Foreman 2008 1 0.01369863
## 10 FVCOM 1 0.01369863
## # ... with 26 more rowsTimestep
data.papers.timestep <- data.all %>% select(Paper_ID,Model_time_step) %>% distinct(Paper_ID,Model_time_step) %>% na.omit()
ggplot(data.papers.timestep,aes(Model_time_step)) + geom_density() + xlab("Time step (seconds)")
Metrics influence
data.timestamp.sr <- select(data.all,Model_time_step,Self_recruitment_mean) %>% na.omit()
ggplot(data.timestamp.sr,aes(Model_time_step,Self_recruitment_mean)) + geom_point()
data.timestamp.lr <- select(data.all,Model_time_step,Local_retention_mean) %>% na.omit()
ggplot(data.timestamp.lr,aes(Model_time_step,Local_retention_mean)) + geom_point()
data.timestamp.ss <- select(data.all,Model_time_step,Settlement_success_mean)
ggplot(na.omit(data.timestamp.ss),aes(Model_time_step,Settlement_success_mean)) + geom_point()
Model resolution and submodels
data.papers.resolution <- data.all %>% select(Paper_ID,Model_resolution_min,Model_spatial_shape) %>% distinct(Paper_ID,Model_resolution_min,Model_spatial_shape)
data.papers.resolution.grid <- filter(data.papers.resolution,Model_spatial_shape == "Grid")
ggplot(data.papers.resolution.grid,aes(Model_resolution_min)) + geom_density() + xlab("The minimumn model resolution (seconds)")
Self-recruitment
data.resolution.sr <- data.all %>% filter(Model_spatial_shape == "Grid") %>%select(Model_resolution_min,Model_spatial_shape,Self_recruitment_mean,Nested_submodels) %>% na.omit()
ggplot(data.resolution.sr,aes(Model_resolution_min,Self_recruitment_mean)) + geom_point()
ggplot(data.resolution.sr,aes(Nested_submodels,Self_recruitment_mean)) + geom_boxplot()+ geom_jitter(width = 0.2)
####Local retention
data.resolution.lr <- data.all %>% filter(Model_spatial_shape == "Grid") %>%select(Model_resolution_min,Nested_submodels,Local_retention_mean) %>% na.omit()
ggplot(data.resolution.lr,aes(Model_resolution_min,Local_retention_mean)) + geom_point()
ggplot(data.resolution.lr,aes(Nested_submodels,Local_retention_mean)) + geom_boxplot()+ geom_jitter(width = 0.2)
####Settlement success
data.resolution.ss <- data.all %>% filter(Model_spatial_shape == "Grid") %>%select(Model_resolution_min,Nested_submodels,Settlement_success_mean) %>% na.omit()
ggplot(data.resolution.ss,aes(Model_resolution_min,Settlement_success_mean)) + geom_point() 
ggplot(data.resolution.ss,aes(Nested_submodels,Settlement_success_mean)) + geom_boxplot()+ geom_jitter(width = 0.2)
Biological models
Pelagic larval durations
ggplot(data.all,aes(x=PLD_fixed)) + geom_density()## Warning: Removed 13 rows containing non-finite values (stat_density).
data.all %>% group_by(PLD_type) %>% summarise (n = n()) %>% mutate(freq = n / sum(n)) %>% na.omit## # A tibble: 3 × 3
## PLD_type n freq
## <chr> <int> <dbl>
## 1 Both 17 0.04941860
## 2 Fixed 302 0.87790698
## 3 Variable 22 0.06395349Comparisons
data.all.lessoutlier <- filter(data.all,PLD_fixed < 150)
ggplot(data.all.lessoutlier,aes(PLD_fixed,Self_recruitment_mean)) + geom_point() ## Warning: Removed 280 rows containing missing values (geom_point).
ggplot(data.all.lessoutlier,aes(PLD_fixed,Local_retention_mean)) + geom_point()## Warning: Removed 304 rows containing missing values (geom_point).
ggplot(data.all.lessoutlier,aes(PLD_fixed,Settlement_success_mean)) + geom_point()## Warning: Removed 281 rows containing missing values (geom_point).
ggplot(data.all.lessoutlier,aes(PLD_fixed,Distance_travelled_mean)) + geom_point() ## Warning: Removed 282 rows containing missing values (geom_point).
Mortality
data.all %>% filter(Mortality == TRUE) %>% group_by(Mortality_function) %>% summarise (n = n()) %>% mutate(freq = n / sum(n))## # A tibble: 7 × 3
## Mortality_function n freq
## <chr> <int> <dbl>
## 1 Decay 11 0.078571429
## 2 Linear 115 0.821428571
## 3 Temp / Salinty / Age dependent 2 0.014285714
## 4 Temperature 7 0.050000000
## 5 Temperature / Depth 1 0.007142857
## 6 Weibull 3 0.021428571
## 7 <NA> 1 0.007142857ggplot(data.all, aes(Mortality, Self_recruitment_mean)) + geom_boxplot() + geom_jitter(width = 0.2)## Warning: Removed 293 rows containing non-finite values (stat_boxplot).## Warning: Removed 293 rows containing missing values (geom_point).
ggplot(data.all, aes(Mortality, Local_retention_mean)) + geom_boxplot()+ geom_jitter(width = 0.2)## Warning: Removed 319 rows containing non-finite values (stat_boxplot).## Warning: Removed 319 rows containing missing values (geom_point).
ggplot(data.all, aes(Mortality, Settlement_success_mean)) + geom_boxplot()+ geom_jitter(width = 0.2)## Warning: Removed 291 rows containing non-finite values (stat_boxplot).## Warning: Removed 291 rows containing missing values (geom_point).
ggplot(data.all, aes(Mortality, Distance_travelled_mean)) + geom_boxplot()+ geom_jitter(width = 0.2)## Warning: Removed 291 rows containing non-finite values (stat_boxplot).
## Warning: Removed 291 rows containing missing values (geom_point).
Sensory extent
ggplot(data.all,aes(x=Sensory_extent)) + geom_density()## Warning: Removed 173 rows containing non-finite values (stat_density).
ggplot(data.all,aes(x=Sensory_extent,y=Self_recruitment_mean)) + geom_point()## Warning: Removed 320 rows containing missing values (geom_point).
ggplot(data.all,aes(x=Sensory_extent,y=Local_retention_mean)) + geom_point()## Warning: Removed 335 rows containing missing values (geom_point).
ggplot(data.all,aes(x=Sensory_extent,y=Settlement_success_mean)) + geom_point()## Warning: Removed 331 rows containing missing values (geom_point).
Proportions of implemented behaviours
data.all %>% group_by(Mortality) %>% summarise (n = n()) %>% mutate(freq = n / sum(n))## # A tibble: 2 × 3
## Mortality n freq
## <lgl> <int> <dbl>
## 1 FALSE 204 0.5930233
## 2 TRUE 140 0.4069767data.all %>% group_by(Growth) %>% summarise (n = n()) %>% mutate(freq = n / sum(n))## # A tibble: 2 × 3
## Growth n freq
## <lgl> <int> <dbl>
## 1 FALSE 321 0.93313953
## 2 TRUE 23 0.06686047data.all %>% group_by(Sensory_ability) %>% summarise (n = n()) %>% mutate(freq = n / sum(n))## # A tibble: 2 × 3
## Sensory_ability n freq
## <lgl> <int> <dbl>
## 1 FALSE 143 0.4156977
## 2 TRUE 201 0.5843023data.all %>% group_by(Settlement_competency_window) %>% summarise (n = n()) %>% mutate(freq = n / sum(n))## # A tibble: 3 × 3
## Settlement_competency_window n freq
## <lgl> <int> <dbl>
## 1 FALSE 167 0.48546512
## 2 TRUE 168 0.48837209
## 3 NA 9 0.02616279data.all %>% group_by(Orientation) %>% summarise (n = n()) %>% mutate(freq = n / sum(n))## # A tibble: 2 × 3
## Orientation n freq
## <lgl> <int> <dbl>
## 1 FALSE 335 0.97383721
## 2 TRUE 9 0.02616279Proportions of swimming behaviours for ALL larvae
larvae.swimming <- filter(data.all,Passive_movement==FALSE)
larvae.swimming %>% group_by(Horizontal_swimming_ability) %>% summarise (n = n()) %>% mutate(freq = n / sum(n))## # A tibble: 2 × 3
## Horizontal_swimming_ability n freq
## <lgl> <int> <dbl>
## 1 FALSE 80 0.8791209
## 2 TRUE 11 0.1208791larvae.swimming %>% group_by(Vertical_swimming_ability) %>% summarise (n = n()) %>% mutate(freq = n / sum(n))## # A tibble: 2 × 3
## Vertical_swimming_ability n freq
## <lgl> <int> <dbl>
## 1 FALSE 86 0.94505495
## 2 TRUE 5 0.05494505larvae.swimming %>% group_by(Ontogentic_vertical_migration) %>% summarise (n = n()) %>% mutate(freq = n / sum(n))## # A tibble: 2 × 3
## Ontogentic_vertical_migration n freq
## <lgl> <int> <dbl>
## 1 FALSE 64 0.7032967
## 2 TRUE 27 0.2967033larvae.swimming %>% group_by(Diel_vertical_migration) %>% summarise (n = n()) %>% mutate(freq = n / sum(n))## # A tibble: 2 × 3
## Diel_vertical_migration n freq
## <lgl> <int> <dbl>
## 1 FALSE 51 0.5604396
## 2 TRUE 40 0.4395604larvae.swimming %>% group_by(Halocline_migration) %>% summarise (n = n()) %>% mutate(freq = n / sum(n))## # A tibble: 2 × 3
## Halocline_migration n freq
## <lgl> <int> <dbl>
## 1 FALSE 90 0.98901099
## 2 TRUE 1 0.01098901larvae.swimming %>% group_by(Circatidal_migration) %>% summarise (n = n()) %>% mutate(freq = n / sum(n))## # A tibble: 2 × 3
## Circatidal_migration n freq
## <lgl> <int> <dbl>
## 1 FALSE 86 0.94505495
## 2 TRUE 5 0.05494505larvae.swimming %>% group_by(Pynocline_migration) %>% summarise (n = n()) %>% mutate(freq = n / sum(n))## # A tibble: 2 × 3
## Pynocline_migration n freq
## <lgl> <int> <dbl>
## 1 FALSE 85 0.93406593
## 2 TRUE 6 0.06593407larvae.swimming %>% group_by(Sinking_velocity) %>% summarise (n = n()) %>% mutate(freq = n / sum(n))## # A tibble: 2 × 3
## Sinking_velocity n freq
## <lgl> <int> <dbl>
## 1 FALSE 87 0.95604396
## 2 TRUE 4 0.04395604larvae.swimming %>% group_by(Egg_buoyancy) %>% summarise (n = n()) %>% mutate(freq = n / sum(n))## # A tibble: 3 × 3
## Egg_buoyancy n freq
## <lgl> <int> <dbl>
## 1 FALSE 2 0.02197802
## 2 TRUE 4 0.04395604
## 3 NA 85 0.93406593Comparisons
Outputs with movement / settlement / orientation
data.compare.metrics <- data.all %>% mutate(settlement = Sensory_extent > 0) %>% mutate(move_orien = movement & Orientation) %>% mutate(orien_settle = Orientation & Sensory_extent > 0) %>% mutate(move_orien_settle = Orientation & Sensory_extent > 0 & movement) %>% mutate(move_settle = Sensory_extent > 0 & movement)
data.compare.metrics <- data.compare.metrics %>% gather(Behaviours, Implemented, Passive_movement,movement,Orientation,settlement,move_orien,orien_settle,move_settle,move_orien_settle) %>% filter(Implemented == TRUE)
ggplot(data.compare.metrics,aes(Behaviours,Self_recruitment_mean)) + geom_boxplot(na.rm = TRUE) + geom_jitter(width=0.2) + coord_flip()## Warning: Removed 467 rows containing missing values (geom_point).
ggplot(data.compare.metrics,aes(Behaviours,Local_retention_mean)) + geom_boxplot(na.rm = TRUE) + geom_jitter(width=0.2) + coord_flip()## Warning: Removed 563 rows containing missing values (geom_point).
ggplot(data.compare.metrics,aes(Behaviours,Settlement_success_mean)) + geom_boxplot(na.rm = TRUE) + geom_jitter(width=0.2) + coord_flip()## Warning: Removed 480 rows containing missing values (geom_point).
Comparisons of different movement methods
data.compare.moving <- data.all %>% gather(movement_factor, implemented, Circatidal_migration, Pynocline_migration, Halocline_migration, Ontogentic_vertical_migration, Vertical_swimming_ability, Horizontal_swimming_ability, Sinking_velocity, Diel_vertical_migration) %>% filter(implemented == TRUE)
ggplot(data.compare.moving,aes(movement_factor,Self_recruitment_mean)) + geom_boxplot(na.rm = TRUE) + geom_jitter(width=0.2) + coord_flip()## Warning: Removed 71 rows containing missing values (geom_point).
ggplot(data.compare.moving,aes(movement_factor,Local_retention_mean)) + geom_boxplot(na.rm = TRUE) + geom_jitter(width=0.2) + coord_flip()## Warning: Removed 97 rows containing missing values (geom_point).
ggplot(data.compare.moving,aes(movement_factor,Settlement_success_mean)) + geom_boxplot(na.rm = TRUE) + geom_jitter(width=0.2) + coord_flip()## Warning: Removed 68 rows containing missing values (geom_point).
Comparisons of different settlement methods
data.compare.settlement <- data.all %>% mutate(settlement,Sensory_extent > 0)
ggplot(data.compare.settlement,aes(settlement,Self_recruitment_mean)) + geom_boxplot(na.rm = TRUE) + geom_jitter(width=0.2) ## Warning: Removed 293 rows containing missing values (geom_point).
ggplot(data.compare.settlement,aes(settlement,Local_retention_mean)) + geom_boxplot(na.rm = TRUE) + geom_jitter(width=0.2) ## Warning: Removed 319 rows containing missing values (geom_point).
ggplot(data.compare.settlement,aes(settlement,Settlement_success_mean)) + geom_boxplot(na.rm = TRUE) + geom_jitter(width=0.2) ## Warning: Removed 291 rows containing missing values (geom_point).
Comparisons of different sensory extent sizes
ggplot(data.compare.settlement, aes(Sensory_extent,Self_recruitment_mean)) + geom_point()## Warning: Removed 320 rows containing missing values (geom_point).
ggplot(data.compare.settlement, aes(Sensory_extent,Local_retention_mean)) + geom_point()## Warning: Removed 335 rows containing missing values (geom_point).
ggplot(data.compare.settlement, aes(Sensory_extent,Settlement_success_mean)) + geom_point()## Warning: Removed 331 rows containing missing values (geom_point).
Comparisons of different sensory extent sizes
##data.compare.settlement <- mutate(data.compare.settlement,settlement_size = Sensory_extent + Settlement_site_size)
ggplot(data.compare.settlement, aes(Settlement_site_size,Self_recruitment_mean)) + geom_point(aes(colour= factor(settlement)))## Warning: Removed 315 rows containing missing values (geom_point).
ggplot(data.compare.settlement, aes(Settlement_site_size,Local_retention_mean)) + geom_point(aes(colour= factor(settlement)))## Warning: Removed 338 rows containing missing values (geom_point).
ggplot(data.compare.settlement, aes(Settlement_site_size,Settlement_success_mean)) + geom_point(aes(colour= factor(settlement)))## Warning: Removed 305 rows containing missing values (geom_point).